Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images

• URL: http://arxiv.org/abs/2405.09697v1
• Date: Wed, 15 May 2024 20:47:59 GMT
• Title: Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Images
• Authors: Jadie Adams, Krithika Iyer, Shireen Elhabian,
• Abstract summary: Correspondence-based statistical shape modeling (SSM) facilitates population-level morphometrics. Recent advancements in deep learning have streamlined this process in inference. We introduce a weakly supervised deep learning approach to predict SSM from images using point cloud supervision.
• Score: 4.424170214926035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anatomical shape analysis plays a pivotal role in clinical research and hypothesis testing, where the relationship between form and function is paramount. Correspondence-based statistical shape modeling (SSM) facilitates population-level morphometrics but requires a cumbersome, potentially bias-inducing construction pipeline. Recent advancements in deep learning have streamlined this process in inference by providing SSM prediction directly from unsegmented medical images. However, the proposed approaches are fully supervised and require utilizing a traditional SSM construction pipeline to create training data, thus inheriting the associated burdens and limitations. To address these challenges, we introduce a weakly supervised deep learning approach to predict SSM from images using point cloud supervision. Specifically, we propose reducing the supervision associated with the state-of-the-art fully Bayesian variational information bottleneck DeepSSM (BVIB-DeepSSM) model. BVIB-DeepSSM is an effective, principled framework for predicting probabilistic anatomical shapes from images with quantification of both aleatoric and epistemic uncertainties. Whereas the original BVIB-DeepSSM method requires strong supervision in the form of ground truth correspondence points, the proposed approach utilizes weak supervision via point cloud surface representations, which are more readily obtainable. Furthermore, the proposed approach learns correspondence in a completely data-driven manner without prior assumptions about the expected variability in shape cohort. Our experiments demonstrate that this approach yields similar accuracy and uncertainty estimation to the fully supervised scenario while substantially enhancing the feasibility of model training for SSM construction.

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